Analytically determining revenue of internet companies using internet metrics

ABSTRACT

With respect to a current quarter of unreported revenue for certain Internet companies, by processes performed by a computer revenue to date is analytically determined and future revenue for the remaining quarter is statistically projected by modeling revenue based on “Internet metrics”. Actual revenue performance is obtained and one or more “Internet metrics” are measured for a given Internet company. Using the revenue and measured Internet metric data from prior quarters, a regression analysis is performed in order to generate multiple models that reflect the relationship between the Internet metrics and revenue. From these models, one is selected that will most likely yield the best revenue estimates. This resultant model and current Internet metric data are subsequently used to estimate the company&#39;s revenue for the current day, week, month, or quarter. These estimates are also used to project the company&#39;s revenue for future days, weeks, months, and quarters.

RELATED APPLICATION

The present application is a divisional of co-pending U.S. patentapplication Ser. No. 10/139,503 filed on May 3, 2002 which claims thebenefit of U.S. Provisional Application No. 60/288,769 filed on May 4,2001, entitled “Methods for Analytically Determining Revenue of InternetCompanies Using Internet Metrics.”

BACKGROUND OF OUR INVENTION

1. Field of the Invention

Our invention relates to methods for analytically determining therevenue of certain types of Internet companies. More particularly, ourinvention relates to methods for using Web based and equipment basedmetrics related to Internet companies for analytically determining thecurrent revenue and statistically projecting the future revenue of thesecompanies.

2. Description of the Background

People are continuing to use the Internet as a medium for communication,education, entertainment, information exchange, electronic commerce(E-commerce), etc. Accordingly, new businesses are emerging andbusinesses in virtually every sector of the economy are using theInternet to provide new services and reach new and existing customersmore effectively and cheaply. In particular, this invention relates tofirms including pure E-commerce companies, “click and mortar” companies,portals, and Internet Service Providers (ISPs). Hereinafter, these typesof companies will be collectively referred to as “Internet” companies.Although there are many other types of companies whose business relatesto the Internet, our focus is on the types of Internet companies justlisted.

The financial community typically does not become aware of the revenuesgenerated by “traditional” companies until several weeks after thecompany quarters end, when revenue data is announced. The same holdstrue for the above “Internet” companies. Although past quarterly data isuseful, the financial community needs daily, weekly, and monthlyinformation, as well as projections to the end of the quarter, to aid intheir everyday decision-making. As such, there is a need by thefinancial community to estimate and forecast the revenue performance ofthe Internet sector. In addition to using information directly providedby companies, financial institutions currently use fundamental andtechnical analysis, such as revenue estimates based on number ofemployees, past sales analysis, and trend analysis of past revenues, toestimate and forecast revenue. However, given both the rate at which theInternet in general is growing and the volatility within the Internetsector, these estimation and forecast techniques are proving to beinadequate. In addition, there is always a need to make more accurateestimates on a more timely basis.

SUMMARY OF OUR INVENTION

It is desirable to provide methods that overcome the shortcomings of theprior art and more accurately estimate and project, on a more timelybasis, the economic performance of an Internet company. Our inventionsatisfies these and other desires by providing a method performed by acomputer for estimating current revenue and projecting future revenue ofan Internet company through Web based and equipment based metricsrelated to that company.

Through experimentation and research, we have discovered that certainphysical events that occur at an Internet company's Web environment andthe amount of certain types of physical equipment used by an Internetcompany are strongly correlated to and predictive of the revenuegenerated by that company. We refer to measures of these physical eventsand physical equipment as “Internet metrics”. Based on our discovery, wehave invented methods for estimating current revenue and projectingfuture revenue of an Internet company, thereby overcoming the issues ofthe prior art. Specifically, we have discovered that at least fourInternet metrics are highly correlated to the revenue generated byInternet companies and when properly modeled, these metrics can be usedto estimate company revenue for the current day, week, month, andquarter, and to project company revenue for future days, weeks, months,and quarters.

The Internet metrics we determined to be predictive of revenue include:the number of page hits at a company's Web site (“page-hits metric”),the number of visitors to a company's Web site (“visitors metric”), thenumber of transactions conducted at a company's Web site (“transactionsmetric”), and the number of Internet hosts (i.e., IP addresses)supported by an Internet Service Provider (“hosts metric”). A fifthmetric, currently under study to verify its correlative nature, is the“delay” within an Internet company's web environment (“delay metric”),which is a measure of how busy the servers, routers, and other equipmentare. Each of these metrics represents a numerical count relative to theduration of time over which the metric is measured. As such, “page-hits”represents the sum, over all visitors, of the number of pages browsed byeach visitor at an Internet company's Web site over each measurementperiod. “Visitors” represents the number of “unique” visitors to visitan Internet company's Web site over each measurement period.“Transactions” represents the number of physical transactions to occurat an Internet company's Web site over each measurement period.“Transactions” is based on “https requests” and is currently measured bycounting all https-requests that begin with “https://”. However,transaction counts can also be determined by counting sub-fields of the“https://” requests. “Hosts” represents the number of IP addressessupported by an ISP over each measurement period. Although we havediscovered that these metrics are strong indicators of revenue, nothingin our invention precludes the use of other Internet metrics to estimaterevenue as these metrics may arise as the Internet industry continues todevelop.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow chart of a method for determining a revenue model foran Internet company in accordance with the present invention.

FIG. 2 is a high level block diagram of a computer, Internet company Website, and processors for collecting Web based and equipment basedmetrics, on which computer can be implemented methods for estimatingcurrent revenue and for projecting future revenue of the Internetcompany based on the collected Web and equipment based metrics inaccordance with the present invention.

FIG. 3 is a chart illustrating weekly revenue estimates for an Internetcompany made using methods in accordance with the present invention.

DETAILED DESCRIPTION

Our inventive method for estimating and projecting revenue comprisesthree general steps, as shown by FIG. 1. In the first step, 102, actualquarterly revenue performance is obtained and one or more of the“Internet metrics” are measured for a given Internet company and aremaintained within an on-going computer database. In some instances, onlyone metric is most relevant to a given company and, in other instances,multiple metrics are most relevant. In the second step, 104, the revenueand measured Internet metric data from prior quarters are used toperform regression analyses in order to generate multiple models thatreflect the relationship between the Internet metrics and revenue. Fromthese models, one is selected that will most likely yield the bestrevenue estimates. In the last step, 106, the resultant model andcurrent Internet metric data are used to estimate the revenue for thecurrent day, week, month, and quarter, and to project the revenue forfuture days, weeks, months, and quarters. Each of these steps is furtherdescribed below. The methods in accordance with embodiments of ourinvention are executed by a computer. For example, as illustrated inFIG. 2, software control for the method steps in accordance with thepresent invention may be stored as software in memory 204 and executedon processor 205 within computer 202.

The first general step of our invention, step 102, requires the on-goingcollection of data points, these data points constituting actualquarterly revenue performance and the Internet metrics. The revenue datais readily available, as it is publicly released following the end of aquarter. The Internet metrics are not as easily obtained althoughvarious methods exist; however, no one method is critical to ourinvention. In general, data points relative to the page-hits,transactions, and visitors metrics are obtained by examining the Webactivity related to consumers browsing an Internet company's Web site.Data points relative to the hosts metric and delay metric are moredifficult to obtain. Several Internet collection methods are brieflydescribed below, which methods can be categorized as direct andindirect. Regardless of the method of collection used, the results areultimately stored in a database, such as a database 206 in computer 202,and are required for steps 104 and 106 of our invention, as describedbelow.

A first collection method is to obtain the data directly from anInternet company. For example, Web servers typically log accessactivity. These logs can be used to determine data points for thetransactions, visitors, and page-hits metrics. Hosts counts can beobtained directly from an ISP's management systems. This data cansubsequently be uploaded to computer 202.

A second collection method is to indirectly obtain the metrics, withoutthe help of an Internet company, as seen in FIG. 2. One such method isto collect the page-hits, transactions, and visitors metrics throughrandom sampling. Under this method, a population set is chosen and eachmember of this set agrees to have his/her personal computer log allInternet activity relative to particular Internet companies. These logsare then analyzed and the results statistically adjusted to representthe public in general. Several companies currently provide suchservices. A second method to gather the page-hits, transactions, andvisitors metrics is to physically monitor a network and gather the data,discarding user specific data. With respect to the delay metric, onemethod is to transmit test packets to a companies web server(s) andmeasure the response time. Sophisticated algorithms are applied to thisresponse time to eliminate time spent within the public Internet networkand to estimate how “busy” the equipment (routers, servers, etc.) withinthe Web environment is. With respect to the hosts metric, U.S. Pat. No.6,178,451 B1, “Computer Network Size Growth Forecasting Method andSystem”, by C. Huitema and S. Weerahandi, describes a method forobtaining an ISP's hosts counts, the teachings of which are incorporatedherein by reference. Nothing in our invention precludes using othermethods for collecting data.

Once collected, the revenue and Internet metric data points arecategorized into three general categories: (1) past actual revenueperformance, (2) past Internet metrics, and (3) current Internetmetrics. With respect to the terms “past” and “current”, “past” data isall data collected up through the most recently reported quarterlyrevenue and “current” data is all data collected since the most recentlyreported quarterly revenue. In accordance with the methods of ourinvention, the past revenue and past Internet metric data are used togenerate a revenue model (step 104) that is subsequently applied to thecurrent Internet metrics to estimate and project revenue (step 106).

Because data collection is on going, at the close of a quarter, thecurrent data becomes past data and is subsequently used to generate amodel for the next quarter. However, our research has shown that due tothe volatility and rate at which the Internet industry is changing,“past” data becomes less predictive of revenue the “older” the past databecomes. As such, in accordance with the methods of our invention, nomore than six quarters of past revenue and Internet metric data are usedto generate the next quarter's model. From a pictorial standpoint, a“moving-window” is placed over the data and advanced by one quarter atthe end of each quarter. However, as the Internet industry stabilizes,nothing in our invention precludes the widening or narrowing of thiswindow to include more or less past data in generating models

As indicated, past revenue is collected on a quarterly basis due to themethods of reporting. For the purposes of discussion, these quarterlydata points can be expressed as a data set as shown in equation (1),where “m” represents the most recently reported quarterly revenue and“n” represents the number of quarters over which the regression analysiswill be performed (as indicated, n is currently set to 6).{R}={ . . . ,R _((m−n)) , . . . ,R _((m−2)) ,R _((m−1)) ,R _(m)}  (1)

With respect to the Internet metrics, each metric represents a count ofa physical event or physical device. Currently, the collection methodsused by our invention measure these events and devices on a weeklybasis, although daily, monthly, or quarterly counts can also be madedepending on the method of collection. Not all metrics apply to allcompanies and therefore not all counts are performed for all companies.Assuming, for discussion purposes, that all five metrics described aboveare collected for a given company on a weekly basis, the set of weeklymetric data points for each metric can be expressed as equations (2-6){D _(trans) }={ . . . ,D _((trans)(k−j)) , . . . ,D _((trans)(k−2)) ,D_((trans)(k−1)) ,D _((trans)k) ,D _((trans)(k+1)) ,D _((trans)(k+2)), .. . }  (2){D _(page-hits) }={ . . . ,D _((page-hits)(k−j)) , . . . ,D_((page-hits)(k−2)) ,D _((page-hits)(k−1)) ,D _((page-hits)k) ,D_((page-hits)(k+1)) ,D _((page-hits)(k+2)), . . . }  (3){D _(visitors) }={ . . . ,D _((visitors)(k−j)) , . . . ,D_((visitors)(k−2)) ,D _((visitors)(k−1)) ,D _((visitors)k) ,D_((visitors)(k+1)) ,D _((visitors)(k+2)), . . . }  (4){D _(hosts) }={ . . . ,D _((hosts)(k−j)) , . . . ,D _((hosts)(k−2)) ,D_((hosts)(k−1)) ,D _((hosts)k) ,D _((hosts)(k+1)) ,D _((hosts)(k+2)), .. . }  (5){D _(delay) }={ . . . ,D _((delay)(k−j)) , . . . ,D _((delay)(k−2)) ,D_((delay)(k−1)) ,D _((delay)k) ,D _((delay)(k+1)) ,D _((delay)(k+2)), .. . }  (6)where the k^(th) data point is the last weekly measurement made for thelast quarter, the (k−j)^(th) data point is the oldest past data pointthat will be used to determine the current model, and the (k+1)^(th),(k+2)^(th), etc. data points are weekly measurements for the currentquarter.

In accordance with the methods of our invention, determination of therevenue models in step 104 below requires that the data pointscomprising the past Internet metrics be expressed on the same scale asthe revenue data. As a result, assuming again that all five metrics arecollected for a given company on a weekly basis, the (k−j)^(th) tok^(th) data points in equations (2)-(6) must be combined and scaled to“quarterly” counts prior to beginning step 104. The result is a new setof “Past” “quarterly” metric data points and can be expressed as shownin equations (7)-(11), where “m” represents the quarterly data pointcorresponding to the most recently reported quarterly revenue and “n”represents the number of quarters over which the regression analysiswill be performed.{P _(trans) }={ . . . ,P _((trans)(m−n)) , . . . ,P _((trans)(m−2)) ,P_((trans)(m−1)) ,P _((trans)m)}  (7){P _(page-hits) }={ . . . ,P _((page-hits)(m−n)) , . . . ,P_((page-hits)(m−2)) ,P _((page-hits)(m−1)) ,P _((page-hits)m)}  (8){P _(visitors) }={ . . . ,P _((visitors)(m−n)) , . . . ,P_((visitors)(m−2)) ,P _((visitors)(m−1)) ,P _((visitors)m)}  (9){P _(hosts) }={ . . . ,P _((hosts)(m−n)) , . . . ,P _((hosts)(m−2)) ,P_((hosts)(m−1)) ,P _((hosts)m)}  (10){P _(delay) }={ . . . ,P _((delay)(m−n)) , . . . ,P _((delay)(m−2)) ,P_((delay)(m−1)) ,P _((delay)m)}  (11)

With respect to estimating revenue using the current Internet metrics,the revenue model resulting from the regression analysis in step 104 isa quarterly model because the regression analysis is performed onquarterly representations of the past data points. As such, if a fullquarter of current metric data has been collected, this data can becombined and scaled to a quarterly count to estimate the currentquarterly revenue. However, in accordance with the methods of ourinvention, the revenue model can also be scaled to daily, weekly, andmonthly revenue models and can be used to estimate revenue for thecurrent day, week, or month by applying corresponding expressions of thecurrent data. The use of the revenue model is further described below instep 106.

Turning to the second general step of our invention, step 104, therevenue data set and past Internet data sets obtained for a givencompany from the data collection step above are next statisticallyanalyzed to generate revenue models of this company. Specifically, steps104-A through 104-D illustrate the steps a computer, for examplecomputer 202 in FIG. 2, would perform to generate revenue models of agiven company and to select a given model to ultimately estimate currentrevenue and project future revenue. Methods in accordance with thepresent invention use regression analysis techniques to generate andselect this model.

As indicated above, depending on the type of Internet company, more thanone type of Internet metric may apply. However, it is not readilyapparent which metric or whether a combination of metrics will providethe “best” prediction of revenue. As such, under methods consistent withour invention, a plurality of revenue models using differentcombinations of the metric variables are first generated and from thesemodels the model most likely to yield the best revenue estimate isdetermined based on statistical characteristics, such as the coefficientof determination (“r²”). Specifically, revenue is first modeled withrespect to each metric independently and then modeled with respect tocombinations of metrics, resulting in a plurality of revenue models. Themodel most likely to yield the “best” revenue estimate is thendetermined and used to estimate current revenue and to project futurerevenue.

For the purpose of discussion, the following discussion assumes thattransactions, page-hits, visitors, and hosts Internet metrics apply to agiven company to be analyzed. However, as indicated above, only one ortwo metrics may be applicable to a given company, in which case fewermodels are generated. In addition, the methods of our invention do notpreclude the use of additional metrics, as these metrics may evolve asthe Internet industry continues to mature. As such, additional modelsmay be generated.

Beginning with step 104-A, a plurality of revenue models is firstgenerated wherein each model uses either a single metric variable ormultiple metric variables, the latter models being generated todetermine if multiple metrics will have statistical characteristics thatwill most likely yield a better estimate of revenue than any one metrictaken individually. Starting with step 104-A1, the individual metricmodels are first generated, where each model has the form of the linearequation:R=aM+b  (12)where “R” is the estimated revenue, “M” is the quarterly Internetmetric, and “a” and “b” are unknown coefficients. While this model canchange in the future as the nature of the E-Commerce industry changes,the model in equation (12) has been shown to provide an accurate fitbetween revenue and the Internet metrics. The result of this first stepis four models of the form:R _((trans))=(a _((trans)))(M _((trans)))+b _((trans))  (13)R _((page-hits))=(a _((page-hits)))(M _((page-hits)))+b_((page-hits))  (14)R _((visitors))=(a _((visitors)))(M _((visitors)))+b _((visitors))  (15)R _((hosts))=(a _((hosts)))(M _((hosts)))+b _((hosts))  (16)

In steps 104-A2 and 104-A3, the “multiple” Internet metric revenuemodels are generated. (Note, as indicated above, this discussion assumesthat more than one Internet metric applies to a given Internet company.If only one metric applies, steps 104-A2 and 104-A3 are never executed,step 104-A1 results in a single model, and this model is subsequentlyused in step 106 below to estimate and forecast revenue.) Our researchhas shown that the Internet metrics may have a collinear relationshipand as such, the variables must be “combined” to address this issue. Wechose to combine the metrics using “standard sums”, whereby the Internetmetrics are standardized using relative unit weights and then added tocreate a new set of Internet metrics. Each new metric represents aunique combination of the original Internet metrics. Note that nothingin our invention precludes the use of other methods, such as principalcomponent analysis, to combine two or more metrics. Using each newmetric, revenue is again modeled multiple times wherein each model hasthe form of the linear equation:R=aM′+b  (17)where “R” is the estimated revenue, “M′” is the new Internet metric, and“a” and “b” are unknown coefficients.

Beginning with step 104-A2, the new set of Internet metrics is createdthrough the “standard sums” technique using combinations of two or moreof the quarterly representations of the existing Internet metrics. Theresult is a new set of metrics, each with a corresponding set of pastquarterly data points. Assuming the presence of four metrics as above,eleven new metrics are created as shown by Table 1, the first columnshowing the new Internet metrics and the second column showing theconstituent Internet metrics that comprise each new metric.

TABLE 1 Combined Internet Metrics New Internet Metric Component Metrics1 P_((trans, page-hits)) P_((trans)), P_((page-hits)) 2P_((trans, visitors)) P_((trans)), P_((visitors)) 3 P_((trans, hosts))P_((trans)), P_((hosts)) 4 P_((page-hits, visitors)) P_((page-hits)),P_((visitors)) 5 P_((page-hits, hosts)) P_((page-hits)), P_((hosts)) 6P_((visitors, , hosts)) P_((visitors)), P_((hosts)) 7P_((trans, page-hits, visitors)) P_((trans)), P_((page-hits)),P_((visitors)) 8 P_((trans, page-hits, hosts)) P_((trans)),P_((page-hits)), P_((hosts)) 9 P_((trans, visitors, hosts)) P_((trans)),P_((visitors)), P_((hosts)) 10 P_((page-hits, visitors, hosts))P_((page-hits)), P_((visitors)), P_((hosts)) 11P_((trans, page-hits, visitors, hosts)) P_((trans)), P_((page-hits)),P_((visitors)), P_((hosts))

Specifically, the “combining” of the metrics by use of “standard sums”is performed by dividing each data point of the constituent pastquarterly Internet metric data sets by a weighting factor and then“summing” corresponding data points (actually, only the most recent “n”elements need be summed). The result is a new metric and correspondingset of “n” past quarterly data points. This procedure is shown below inequations (18), (24), and (28) for the “P_((trans,page-hits))”,“P_((trans,page-hits,visitors))”, and“P_((trans,page-hits,visitors, hosts))” metrics respectively. The othereight metrics are similarly defined by equations (19) to (23) and (25)to (27), not shown.

$\begin{matrix}\begin{matrix}{\left\{ P_{({{trans},{{page} - {hits}}})} \right\} = {\left\{ \frac{\left\{ P_{({trans})} \right\}}{W_{({trans})}} \right\} + \left\{ \frac{\left\{ P_{({{page} - {hits}})} \right\}}{W_{({trans})}} \right\}}} \\{= \left\{ {\left( {\frac{P_{{({trans})}{({m - n})}}}{W_{({trans})}} + \frac{P_{{({{page} - {hits}})}{({m - n})}}}{W_{({{page} - {hits}})}}} \right),\ldots\mspace{14mu},\left( {\frac{P_{{({trans})}m}}{W_{({trans})}} + \frac{P_{{({{page} - {hits}})}m}}{W_{({{page} - {hits}})}}} \right)} \right\}} \\{{~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~}\vdots}\end{matrix} & (18) \\\begin{matrix}{\left\{ P_{({{trans},{{page} - {hits}},{visitors}})} \right\} = {\left\{ \frac{\left\{ P_{({trans})} \right\}}{W_{({trans})}} \right\} + \left\{ \frac{\left\{ P_{({{page} - {hits}})} \right\}}{W_{({{page} - {hits}})}} \right\} + \left\{ \frac{\left\{ P_{({visitors})} \right\}}{W_{({visitors})}} \right\}}} \\{= \left\{ {\left( {\frac{P_{{({trans})}{({m - n})}}}{W_{({trans})}} + \frac{P_{{({{page} - {hits}})}{({m - n})}}}{W_{({{page} - {hits}})}} + \frac{P_{{({visitors})}{({m - n})}}}{W_{({visitors})}}} \right),\ldots\mspace{14mu},\left( {\frac{P_{{({trans})}m}}{W_{({trans})}} + \frac{P_{{({{page} - {hits}})}m}}{W_{({{page} - {hits}})}} + \frac{P_{{({visitors})}m}}{W_{({visitors})}}} \right)} \right\}} \\{{~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~}\vdots}\end{matrix} & (24) \\\begin{matrix}\left\{ {P_{{({{trans},{{page} - {hits}},{visitors},{hosts}})}\}} = {\left\{ \frac{\left\{ P_{({trans})} \right\}}{W_{({trans})}} \right\} + \left\{ \frac{\left\{ P_{({{page} - {hits}})} \right\}}{W_{({{page} - {hits}})}} \right\} + \left\{ \frac{\left\{ P_{({visitors})} \right\}}{W_{({visitors})}} \right\} + \left\{ \frac{\left\{ P_{({hosts})} \right\}}{W_{({hosts})}} \right\}}} \right\} \\{= \begin{Bmatrix}{\left( {\frac{P_{{({trans})}{({m - n})}}}{W_{({trans})}} + \frac{P_{{({{page} - {hits}})}{({m - n})}}}{W_{({{page} - {hits}})}} + \frac{P_{{({visitors})}{({m - n})}}}{W_{({visitors})}} + \frac{P_{{({hosts})}{({m - n})}}}{W_{({hosts})}}} \right\},\ldots\mspace{14mu},} \\\left( {\frac{P_{{({trans})}m}}{W_{({trans})}} + \frac{P_{{({{page} - {hits}})}m}}{W_{({{page} - {hits}})}} + \frac{P_{{({visitors})}m}}{W_{({visitors})}} + \frac{P_{{({hosts})}m}}{W_{({hosts})}}} \right)\end{Bmatrix}}\end{matrix} & (28)\end{matrix}$where “W_((trans))”, “W_((visitors))”, and “W_((page-hits))” are theweighting factors. Our invention currently defines the weighting factoras the standard deviation of each Internet metric data set, equations(7)-(10), over the “n” most recent values. Our research has shown thatthe collinearity between the metrics is adequately accounted for byusing standard deviation as the weighting factor. However, our inventiondoes not preclude the use of other weighting factors. The “W_((trans))”,“W_((visitor))”, and “W_((page-hits))” “W_((hosts))” weighting factorsare shown in equations (29)-(32) below.

$\begin{matrix}{W_{({trans})} = {\sigma_{({trans})} = \frac{{\sum\limits_{i = {m - n}}^{m}\left( P_{{({trans})}i} \right)^{2}} - {(n)\left( \overset{\_}{P_{({trans})}} \right)^{2}}}{\left( {n - 1} \right)}}} & (29) \\\begin{matrix}{W_{({{page} - {hits}})} = \sigma_{({{page} - {hits}})}} \\{= \frac{{\sum\limits_{i = {m - n}}^{m}\left( P_{{({{page} - {hits}})}i} \right)^{2}} - {(n)\left( \overset{\_}{P_{({{page} - {hits}})}} \right)^{2}}}{\left( {n - 1} \right)}}\end{matrix} & (30) \\\begin{matrix}{W_{({visitors})} = \sigma_{({visitors})}} \\{= \frac{{\sum\limits_{i = {m - n}}^{m}\left( P_{{({visitors})}i} \right)^{2}} - {(n)\left( \overset{\_}{P_{({visitors})}} \right)^{2}}}{\left( {n - 1} \right)}}\end{matrix} & (31) \\{W_{({hosts})} = {\sigma_{({hosts})} = \frac{{\sum\limits_{i = {m - n}}^{m}\left( P_{{({hosts})}i} \right)^{2}} - {(n)\left( \overset{\_}{P_{({hosts})}} \right)^{2}}}{\left( {n - 1} \right)}}} & (32)\end{matrix}$where “ P_((trans)) ”, P_((page-hits)) , P_((visitors)) , andP_((hosts)) are the average transactions, page-hits, visitors, and hostsmetric values as computed over the “n” most recent data elements inequations (7)-(10), respectively.

In step 104-A3, the eleven new metrics are each modeled as a linearequation resulting in eleven additional models, three of which,“R_((trans,page-hits))”, “R_((trans,page-hits,visitors))”, and“R_((trans,page-hits,visitors, hosts))” are shown below in equations(33), (39), and (43). The remaining eight equations are similarlydefined by equations (34) to (38) and (40) to (42), not shown.R _((trans,page-hits))=(a _((trans,page-hits)))(M_((trans,page-hits)))+b _((trans,page-hits))  (33)R _((trans,page-hits,visitors))=(a _((trans,page-hits,visitors)))(M_((trans,page-hits,visitors)))+b _((trans,page-hits,visitors))  (39)R _((trans,page-hits,visitors,hosts))=(a_((trans,page-hits,visitors,hosts)))(M_((trans,page-hits,visitors,hosts)))+b_((trans,page-hits,visitors,hosts))  (43)

In step 104-B, the “least squares line” or “regression line” isdetermined for each individual and multiple metric model, (13)-(16) and(33)-(43), by determining the least squares estimate for each of themodel coefficients: “a_((trans))”, “b_((trans))”,“a_((trans,page-hits))”, “b_((trans,page-hits))”,“a_((trans,page-hits, visitors))”, etc. Using the revenue data setequation (1), the past individual metric data sets equations (7)-(10),and the new combined metric data sets equations (18)-(28), the leastsquares estimate of each coefficient is determined, as shown inequations (44)-(75) for the “a_((trans))”, “b_((trans))”,“a_((trans, page-hits))”, “b_((trans, page-hits))”,“a_((trans, page-hits, visitors, hosts))”,“b_((trans, page-hits, visitors, hosts))” coefficients. The leastsquares estimate equations for the remaining twenty-four coefficientsare similarly defined by equations (46) to (51) and (54) to (73), notshown

$\begin{matrix}{{\hat{a}}_{({trans})} = \frac{{\sum\limits_{i = {m - n}}^{m}{\left( P_{{({trans})}i} \right)\left( R_{i} \right)}} - {(n)\left( \overset{\_}{P_{trans}} \right)\left( \overset{\_}{R} \right)}}{{\sum\limits_{i = {m - n}}^{m}\left( P_{{({trans})}i} \right)^{2}} - {(n)\left( \overset{\_}{P_{({trans})}} \right)^{2}}}} & (44) \\{{{\hat{b}}_{({trans})} = {\left( \overset{\_}{R} \right) - {\left( {\hat{a}}_{({trans})} \right)\left( \overset{\_}{P_{({trans})}} \right)}}}\vdots} & (45) \\{{\hat{a}}_{({{trans},{{page} - {hits}}})} = \frac{\begin{matrix}{{\sum\limits_{i = {m - n}}^{m}{\left( P_{{({{trans},{{page} - {hits}}})}i} \right)\left( R_{i} \right)}} -} \\{(n)\left( \overset{\_}{P_{({{trans},{{page} - {hits}}})}} \right)\left( \overset{\_}{R} \right)}\end{matrix}}{\begin{matrix}{{\sum\limits_{i = {m - n}}^{m}\left( P_{{({{trans},{{page} - {hits}}})}i} \right)^{2}} -} \\{(n)\left( \overset{\_}{P_{({{trans},{{page} - {hits}}})}} \right)^{2}}\end{matrix}}} & (52) \\{{{\hat{b}}_{({{trans},{{page} - {hits}}})} = {\left( \overset{\_}{R} \right) - {\left( {\hat{a}}_{({{trans},{{page} - {hits}}})} \right)\left( \overset{\_}{P_{({{trans},{{page} - {hits}}})}} \right)}}}\vdots} & (53) \\{{\hat{a}}_{({{trans},\;{{page}\; - \;{hits}},\;{visitors},\;{hosts}})} = \frac{\begin{matrix}{{\sum\limits_{i = {m - n}}^{\; m}{\left( P_{{({{trans},{{page} - {hits}},{visitors},{hosts}})}\; i} \right)\;\left( R_{\; i} \right)}}\; -} \\{(n)\;\left( \overset{\_}{P_{({{trans},{{page} - {hits}},{visitors},{hosts}})}} \right)\;\left( \overset{\_}{R} \right)}\end{matrix}}{\begin{matrix}{{\sum\limits_{i = {m - n}}^{\; m}\;\left( P_{{({{trans},{{page} - {hits}},{visitors},{hosts}})}\; i} \right)^{2}}\; -} \\{(n)\;\left( \;\overset{\_}{P_{({{trans},{{page} - {hits}},{visitors},{hosts}})}} \right)^{2}}\end{matrix}}} & (74) \\{{\hat{b}}_{({{trans},{{page} - {hits}},{visitors},{hosts}})} = {\left( \overset{\_}{R} \right) - {\left( {\hat{a}}_{({{trans},{{page} - {hits}},{visitors},{hosts}})} \right)\left( \overset{\_}{P_{{{trans},{{page} - {hits}},{visitors},{hosts}})}} \right)}}} & (75)\end{matrix}$where “n” is the number of past data points in the revenue and metricdata sets deemed to be predictive of the current revenue (as indicatedabove, n=6 quarters is currently used), “P_((trans)i)”“P_((trans, page-hits)i)”, “P_((trans, page-hits, visitors, hosts)i)”,etc. are data points from the original and new metric data setsequations (7)-(10) and (18)-(28), “R_(i)” is data points from therevenue data set equation (1), “ P_((trans)) ”, “ P_((trans, page-hits))”, “ P_((trans, page-hits, visitors, hosts)) ”, etc. are the“average-metric-value” of each original/new metric data set as computedover the “n” most recent data elements in equations (7)-(10) and(18)-(28), and “ R” is the “average revenue value” as computed over the“n” most recent data elements in equation (1). The result of step 104-Bis fifteen revenue estimation equations as shown in equations (76)-(90)((81) to (85) and (87) to (89) not being shown) and represented byModels 150 in FIG. 1.{circumflex over (R)} _((trans))=(â _((trans)))(M_((trans)))+{circumflex over (b)} _((trans))  (76){circumflex over (R)} _((page-hits))=(â _((page-hits)))(M_((page-hits)))+{circumflex over (b)} _((page-hits))  (77){circumflex over (R)} _((visitors))=(â _((visitors)))(M_((visitors)))+{circumflex over (b)} _((visitors))  (78){circumflex over (R)} _((hosts))=(â _((hosts)))(M_((hosts)))+{circumflex over (b)} _((hosts))  (79){circumflex over (R)} _((trans,page-hits))=(â _((trans,page-hits)))(M_((trans,page-hits)))+{circumflex over (b)} _((trans,page-hits))  (80){circumflex over (R)} _((trans,page-hits, visitors))=(â_((trans,page-hits,visitors)))(M_((trans,page-hits,visitors)))+{circumflex over (b)}_((trans,page-hits,visitors))  (86){circumflex over (R)} _((trans,page-hits,visitors,hosts))=(â_((trans,page-hits,visitors,hosts)))(M_((trans,page-hits,visitors,hosts)))+{circumflex over (b)}_((trans,page-hits,visitors,hosts))  (90)Each of these equations can be used to estimate the current quarter'srevenue if quarterly representations of the combined metrics areavailable (i.e., a full quarter of data points have been collected).

As indicated, the completion of step 104-B results in a plurality ofindividual metric and multiple metric revenue models, as shown by theequations above. The next step is to determine which of these models hasthe statistical properties to likely be the “best” estimator of currentrevenue. Different methods exist in the art for determining how well a“least squares equation” performs. One method used by our invention isto compute the “coefficient of determination”, also called “r²”, for theequation, although nothing precludes the use of other methods. Thecoefficient of determination for any least squares equation ranges invalue between “0” and “1”, with “0” indicating a weak model fit and “1”indicating a strong model fit.

Beginning with step 104-C, the coefficient of determination is computedfor each of the determined metric models, equations (76)-(90). The modelwith the largest resultant “value” is then chosen, in step 104-D, as themodel to estimate current revenue. The equations to compute thecoefficient of determination for “{circumflex over (R)}_((trans))”,“{circumflex over (R)}_((trans, page-hits))”, “{circumflex over(R)}_((trans, page-hits, visitors))”, and “{circumflex over(R)}_((trans, page-hits, visitors, hosts))” are shown below in equations(91), (95), (101), and (105). The remaining eleven equations, (92) to(94), (96) to (100), and (102) to (104), are similarly defined.

$\begin{matrix}{{r_{({trans})}^{2} = {1 - \frac{\sum\limits_{i = {m - n}}^{m}\left( {R_{i} - {\hat{R}}_{{({trans})}i}} \right)^{2}}{\sum\limits_{i = {m - n}}^{m}\left( {R_{i} - \overset{\_}{R}} \right)^{2}}}}\vdots} & (91) \\{{r_{({{trans},{{page} - {hits}}})}^{2} = {1 - \frac{\sum\limits_{i = {m - n}}^{m}\left( {R_{i} - {\hat{R}}_{{({{trans},{{page} - {hits}}})}i}} \right)^{2}}{\sum\limits_{i = {m - n}}^{m}\left( {R_{i} - \overset{\_}{R}} \right)^{2}}}}\vdots} & (95) \\{{r_{({{trans},{{page} - {hits}},{visitors}})}^{2} = {1 - \frac{\sum\limits_{i = {m - n}}^{m}\left( {R_{i} - {\hat{R}}_{{({{trans},{{page} - {hits}},{visitors}})}i}} \right)^{2}}{\sum\limits_{i = {m - n}}^{m}\left( {R_{i} - \overset{\_}{R}} \right)^{2}}}}\vdots} & (101) \\{r_{({{trans},{{page} - {hits}},{visitors},{hosts}})}^{2} = {1 - \frac{\sum\limits_{i = {m - n}}^{m}\left( {R_{i} - {\hat{R}}_{{({{trans},{{page} - {hits}},{visitors},{hosts}})}i}} \right)^{2}}{\sum\limits_{i = {m - n}}^{m}\left( {R_{i} - \overset{\_}{R}} \right)^{2}}}} & (105)\end{matrix}$where “{circumflex over (R)}_((trans)i)” is the estimated revenue usingthe “n” most recent data points from the transactions metric data setequation (7), “{circumflex over (R)}_((trans, page-hits)i)” is theestimated revenue using the “n” most recent data points from thecombined transaction/page-hits metric data set equation (18), etc.

In step 104-D, the model with largest coefficient of determination ischosen as the model that will most likely provide the best estimate ofcurrent revenue. This model, for discussion purposes, will be referredto as:{circumflex over (R)}=âM′+{circumflex over (b)}  (106)where “â” and “{circumflex over (b)}” are the “a” and “b” least squaresestimate coefficients from the chosen model, and “M′” is the metric(either single or multiple) of the chosen model. In another embodimentof our invention, the individual or multiple metric used to make priorrevenue estimates is also considered in step 104-D when choosing thepresent model.

Turning to the third general step of our invention, step 106, equation(106) can now be used to estimate current revenue and to statisticallyproject future revenue of the modeled Internet company. Revenueestimation will first be described followed by revenue projection.

Equation (106) can be used to estimate a company's current revenue overa given period of time based on current measurements of the M′ metric.M′ is either an individual or multiple metric. Assume first that M′ isan individual metric. As indicated above, the collection methodscurrently used by our invention measure the metrics on a weekly basis,although daily, monthly, and quarterly measurements can also be made.Assuming weekly measurements are made, the (k+1)^(th), (k+2)^(th), etc.data points from the metric data sets, equations (2)-(6), can now beused to estimate revenue. Specifically, if a full quarter of weeklymeasurements have been made (e.g., thirteen measurements), the resultantdata points can be combined and scaled to a quarterly count andsubstituted for M′ in equation (106) to estimate revenue for the currentquarter. However, a more useful application of our invention is toestimate revenue as soon as possible. As such, under methods consistentwith our invention, equation (106) can be scaled to estimate revenue forthe current week and month, as shown by equations (107) and (108),respectively, were “x_(week)” is the number of weeks in the quarter and“x_(month)” is the number of months in the quarter.

$\begin{matrix}{{\hat{R}}_{week} = {{\frac{\hat{a}}{x_{week}}M^{\prime}} + \frac{\hat{b}}{x_{week}}}} & (107) \\{{\hat{R}}_{month} = {{\frac{\hat{a}}{x_{month}}M^{\prime}} + \frac{\hat{b}}{x_{month}}}} & (108)\end{matrix}$Hence, using equation (107), the weekly metric data points can be usedto estimate revenue on a week-by-week basis. By combining and scalingthe weekly data points to monthly counts, equation (108) can be used toestimate revenue on a monthly basis. Similarly, if the metric ismeasured on a daily basis, equation (106) can be scaled to estimatedaily revenue.

Assume next that M′ is a multiple metric and, for discussion purposes,is a combination of the “transactions” and “page-hits” metrics. Similarto the individual metric, the combined metric can be used to estimaterevenue for the current week, month, and quarter through equations(106), (107), and (108). However, similar to step 104-A2 above, the(k+1)^(th), (k+2)^(th), etc. data points of the “{D_(trans)}” and“{D_(page-hits)}” data sets, equations (2) and (3), cannot be applied tothe revenue equations until these data points are combined usingprincipals similar to equation (18) (i.e., standard sums).

As such, under methods consistent with our invention, the “{D_(trans)}”and “{D_(page-hits)}” data sets are first individually expressed asquarterly data points, monthly data points, or maintained as weekly datapoints, depending on the desired estimate, using all data points fromthe (k−j)^(th) through the current measurement. Next, using the“standard sum” principals set forth in step 104-A2, a weighting factoris determined for each of the resultant data sets using the standarddeviation of these data sets. Finally, the data points of the resultantdata sets are weighted and corresponding points are summed, resulting ina new combined data set that can be applied to equations (106), (107),and (108) to estimate current revenue.

For example, using equation (107) to estimate revenue for the currentweek, equations (109) and (110) show the weekly weighting factors forthe “{D_(trans)}” and “{D_(page-hits)}” data sets

$\begin{matrix}\begin{matrix}{W_{({trans})} = \sigma_{({trans})}} \\{= \frac{{\sum\limits_{i = {k - j}}^{k + g}\left( D_{{({trans})}i} \right)^{2}} - {\left( {j + g} \right)\left( \overset{\_}{D_{({trans})}} \right)^{2}}}{\left( {j + g - 1} \right)}}\end{matrix} & (109) \\\begin{matrix}{W_{({{page} - {hits}})} = \sigma_{({{page} - {hits}})}} \\{= \frac{{\sum\limits_{i = {k - j}}^{k + g}\left( D_{{({{page} - {hits}})}i} \right)^{2}} - {\left( {j + g} \right)\left( \overset{\_}{D_{({{page} - {hits}})}} \right)^{2}}}{\left( {j + g - 1} \right)}}\end{matrix} & (110)\end{matrix}$where (k+g) is the most current weekly data point, and “ D_((trans)) ”and “ D_((page-hits)) ” are average weekly metric values over the(k−j)^(th) to (k+g)^(th) data elements. Using these weighting factors,the resultant combined data set is shown in equation (111), the datapoints of which can be used with equation (107) to estimate the revenuefor each week.

$\begin{matrix}\begin{matrix}{\left\{ C_{({{trans},{{page} - {hits}}})} \right\} = {\left\{ \frac{\left\{ D_{({trans})} \right\}}{W_{({trans})}} \right\} + \left\{ \frac{\left\{ D_{({{page} - {hits}})} \right\}}{W_{({trans})}} \right\}}} \\{= \left\{ {\left( {\frac{D_{{({trans})}{({k - j})}}}{W_{({trans})}} + \frac{D_{{({{page} - {hits}})}{({k - j})}}}{W_{({{page} - {hits}})}}} \right),\ldots\mspace{14mu},\left( {\frac{D_{{({trans})}{({k + g})}}}{W_{({trans})}} + \frac{D_{{({{page} - {hits}})}{({k + g})}}}{W_{({{page} - {hits}})}}} \right)} \right\}}\end{matrix} & (111)\end{matrix}$

In accordance with the methods of our invention, FIG. 3 shows weeklyrevenue estimates and actual quarterly revenue results for four quartersfor an Internet company. The thirteen points comprising quarters 302,304, 306, and 308 represent weekly revenue estimates using the methodsof our invention. Once a full quarter of revenue estimates are made, theresultant values can be summed to estimate the quarterly revenue, priorto the actual revenue being reported. (As reference, bars 320, 322, 324,and 326 represent actual reported quarterly revenue.)

In addition to estimating revenue for the current day, week, or month,it is also useful, in advance of the availability of metric data forfuture time periods, to project revenue for the remaining days, weeks,or months of the quarter and to subsequently use the estimated andprojected values to project the quarterly revenue as a whole. Forexample, the five points comprising quarter 310 represent weekly revenueestimates for the current quarter. These points can be used tostatistically project the revenue for each of the remaining eight weekscomprising the quarter. Subsequently, the five revenue estimates and theeight revenue projections can be summed to project the quarterlyrevenue, as shown by bar 328. Under methods consistent with ourinvention, a “running average technique” is used to make these forecaststhereby capturing the trend of the revenue estimates, however nothingprecludes the use of other projection techniques.

More explicitly, the running average technique is used to statisticallyproject the revenue for the remaining eight weeks of quarter 310 asfollows. The sixth weekly revenue point is projected by averaging theprior N weekly revenue points beginning with the fifth week. The seventhweekly revenue point is projected by averaging the prior N weeklyrevenue points beginning with the sixth week (i.e., the projected sixthweek is used to project the seventh week). This method is continueduntil the thirteenth weekly revenue point is projected by averaging theprior N weekly revenue points beginning with the twelfth week. Thequarterly revenue is then projected by summing all thirteen weeks, thisprojection being represented by bar 328 in FIG. 3. This embodiment ofour invention currently uses N=6 although other values can be used.

The above-described embodiment of our invention is intended to beillustrative only. Numerous other embodiments may be devised by thoseskilled in the art without departing from the spirit and scope of ourinvention.

We claim:
 1. A method for estimating current revenue of a company, saidmethod comprising: a computing device obtaining from network resourcesdata points associated with the company for at least two Internetmetrics over at least first and second past time periods; the computingdevice creating past Internet metric data points for the at least firstand second past time periods by combining the data points from the atleast two Internet metrics for respective periods of the at least firstand second past time periods; the computing device generating one ormore revenue models for the company by correlating at least the createdpast Internet metric data points for the first past time period to pastrevenue data points of the company for the first past time period andthe created past Internet metric data points for the second past timeperiod to past revenue data points of the company for the second pasttime period; the computing device determining statisticalcharacteristics of the one or more revenue models respectively; thecomputing device selecting a chosen revenue model based on thestatistical characteristics of the chosen revenue model; the computingdevice creating one or more new Internet metric data points for acurrent time period by combining one or more current data pointsobtained for the at least two Internet metrics from the current timeperiod; and the computing device estimating the current revenue of thecompany by applying the created new Internet metric data points to thechosen revenue model.
 2. The method of claim 1 wherein creating one ormore new Internet metric data points includes creating the new Internetmetric data points through standard sums.
 3. The method of claim 2further comprising using standard deviations as a weighting factor inthe standard sums.
 4. The method of claim 1 wherein generating one ormore revenue models includes generating one or more quarterly revenuemodels, the method further comprising the computing device scaling theone or more quarterly revenue models to weekly revenue models, andwherein creating one or more new Internet metric data points includescreating one week of data points, and wherein estimating the company'scurrent revenue includes estimating revenue for the one week.
 5. Themethod of claim 1 wherein combining the data points from the at leasttwo Internet metrics comprises combining at least two of a page-hitsmetric, a visitors metric, a transactions metric, or a hosts metric. 6.The method of claim 1 wherein obtaining data points from networkresources includes obtaining data points from at least one of domainname servers, web activity metrics at a customer premises level, or webactivity metrics at a web site.
 7. The method of claim 1 wherein thecomputing device generating the one or more revenue models for thecompany comprises the computing device performing a regression analysison the created past internet metric data points and past revenue datapoints of the company for the first past time period and the createdpast Internet metric data points and past revenue data points of thecompany for the second past time period.
 8. A tangible computer readablemedium, having instructions stored thereon, that in response toexecution by a device, cause the device to perform operationscomprising: obtaining data points associated with a company from networkresources for at least two Internet metrics over at least first andsecond past time periods, creating past Internet metric data points forthe at least first and second past time periods by combining the datapoints from the at least two Internet metrics for respective periods ofthe at least first and second past time periods; generating one or morerevenue models for the company by correlating at least the created pastInternet metric data points for the first past time period to pastrevenue data points of the company for the first past time period andthe created past Internet metric data points for the second past timeperiod to past revenue data points of the company for the second pasttime period; determining statistical characteristics of the one or morerevenue models respectively; selecting a chosen revenue model based onthe statistical characteristics of the chosen revenue model; creatingone or more new Internet metric data points for a current time period bycombining one or more current data points obtained for the at least twoInternet metrics from the current time period; and estimating thecurrent revenue of the company by applying the created new Internetmetric data points to the chosen revenue model.
 9. The tangible computerreadable medium of claim 8 wherein the new Internet metric data pointsare created through standard sums.
 10. The tangible computer readablemedium of claim 9 wherein standard deviations are used as a weightingfactor in the standard sums.
 11. The tangible computer readable mediumof claim 8 wherein the chosen revenue model is a quarterly revenuemodel, the operations further comprising scaling the chosen revenuemodel to a weekly revenue model, and wherein the created new Internetmetric data points comprise one week of data points, and wherein theestimated company's current revenue is revenue for the one week.
 12. Thetangible computer readable medium of claim 8 wherein the at least twoInternet metrics comprise at least two of a page-hits metric, a visitorsmetric, a transactions metric, or a hosts metric.
 13. The tangiblecomputer readable medium of claim 8 wherein the network resourcescomprise at least one of domain name servers, web activity metrics at acustomer premises level, or web activity metrics at a web site.
 14. Thetangible computer readable medium of claim 8 wherein generating one ormore revenue models comprises performing a regression analysis on thecreated past Internet metric data points and past revenue data points ofthe company for the first past time period and the created past Internetmetric data points and past revenue data points of the company for thesecond past time period.
 15. An apparatus for estimating the currentrevenue of a company, the apparatus comprising: a computing deviceconfigured to: receive past data points of at least two internet metricsassociated with the company for at least first and second past timeperiods and current data points of the at least two internet metrics fora current time period from network resources; create a combination ofthe past data points for the at least first and second past time periodsrespectively; generate one or more revenue models for the company bycorrelating at least the combination of the past data points for thefirst past time period to past revenue data points of the company forthe first past time period and the combination of the past data pointsfor the second past time period to past revenue data points of thecompany for the second past time period; determine statisticalcharacteristics of the one or more revenue models respectively; select achosen revenue model based on the statistical characteristics of thechosen revenue model; and estimate a current revenue for the company byapplying a combination of the current data points to the chosen revenuemodel.
 16. The apparatus of claim 15 wherein the computing device isconfigured to receive past and current data points from networkresources that comprise at least one of domain name servers, webactivity metrics at a customer premises level, or web activity metricsat a web site.
 17. The apparatus of claim 15 wherein the computingdevice is further configured to apply weighting factors to create thecombination of the past data points, the at least two Internet metricscomprising at least two of a page-hits metric, a visitors metric, atransactions metric, or a hosts metric.
 18. The apparatus of claim 15wherein the computing device is further configured to create thecombination of the current data points using standard sums.
 19. Theapparatus of claim 18 wherein the computing device is further configuredto use standard deviations as a weighting factor in the standard sums.20. The apparatus of claim 15 wherein the computing device configured togenerate one or more revenue models for the company comprises thecomputing device configured to perform a regression analysis on thecombination of the past data points and the past revenue data points ofthe company for the first past time period and the second past timeperiod respectively.